Overview

Dataset statistics

Number of variables26
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Numeric7
Categorical9
Boolean10

Alerts

Age is highly overall correlated with AgeGroup_25-34 and 3 other fieldsHigh correlation
AgeGroup_25-34 is highly overall correlated with Age and 1 other fieldsHigh correlation
AgeGroup_35-49 is highly overall correlated with Age and 1 other fieldsHigh correlation
AgeGroup_50-64 is highly overall correlated with AgeHigh correlation
AgeGroup_65+ is highly overall correlated with AgeHigh correlation
Age_Balance_interaction is highly overall correlated with Balance and 2 other fieldsHigh correlation
Balance is highly overall correlated with Age_Balance_interaction and 3 other fieldsHigh correlation
BalanceSalaryRatio is highly overall correlated with Age_Balance_interaction and 1 other fieldsHigh correlation
CreditScore is highly overall correlated with CreditScoreBucket_High and 2 other fieldsHigh correlation
CreditScoreBucket_High is highly overall correlated with CreditScoreHigh correlation
CreditScoreBucket_Med is highly overall correlated with CreditScoreHigh correlation
CreditScoreBucket_VeryHigh is highly overall correlated with CreditScoreHigh correlation
EstimatedSalary is highly overall correlated with LowSalary_HighBalanceHigh correlation
HighBalanceFlag is highly overall correlated with Age_Balance_interaction and 2 other fieldsHigh correlation
LowSalary_HighBalance is highly overall correlated with Balance and 2 other fieldsHigh correlation
MultipleProducts is highly overall correlated with NumOfProductsHigh correlation
NumOfProducts is highly overall correlated with MultipleProductsHigh correlation
Tenure is highly overall correlated with TenureBucket_2-3 and 2 other fieldsHigh correlation
TenureBucket_2-3 is highly overall correlated with TenureHigh correlation
TenureBucket_4-6 is highly overall correlated with TenureHigh correlation
TenureBucket_7-10 is highly overall correlated with TenureHigh correlation
AgeGroup_50-64 is highly imbalanced (53.2%)Imbalance
AgeGroup_65+ is highly imbalanced (82.4%)Imbalance
BalanceSalaryRatio is highly skewed (γ1 = 94.26402235)Skewed
HighBalanceFlag is uniformly distributedUniform
Tenure has 413 (4.1%) zerosZeros
Balance has 3617 (36.2%) zerosZeros
BalanceSalaryRatio has 3617 (36.2%) zerosZeros

Reproduction

Analysis started2025-12-15 06:24:58.261915
Analysis finished2025-12-15 06:25:14.513080
Duration16.25 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

CreditScore
Real number (ℝ)

High correlation 

Distinct460
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.5288
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:25:14.620494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile489
Q1584
median652
Q3718
95-th percentile812
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.653299
Coefficient of variation (CV)0.14857651
Kurtosis-0.42572568
Mean650.5288
Median Absolute Deviation (MAD)67
Skewness-0.071606608
Sum6505288
Variance9341.8602
MonotonicityNot monotonic
2025-12-15T06:25:14.752210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850233
 
2.3%
67863
 
0.6%
65554
 
0.5%
70553
 
0.5%
66753
 
0.5%
68452
 
0.5%
65150
 
0.5%
67050
 
0.5%
68348
 
0.5%
65248
 
0.5%
Other values (450)9296
93.0%
ValueCountFrequency (%)
3505
0.1%
3511
 
< 0.1%
3581
 
< 0.1%
3591
 
< 0.1%
3631
 
< 0.1%
3651
 
< 0.1%
3671
 
< 0.1%
3731
 
< 0.1%
3762
 
< 0.1%
3821
 
< 0.1%
ValueCountFrequency (%)
850233
2.3%
8498
 
0.1%
8485
 
0.1%
8476
 
0.1%
8465
 
0.1%
8456
 
0.1%
8447
 
0.1%
8432
 
< 0.1%
8427
 
0.1%
84112
 
0.1%

Age
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.9218
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:25:14.887669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.487806
Coefficient of variation (CV)0.26945841
Kurtosis1.3953471
Mean38.9218
Median Absolute Deviation (MAD)6
Skewness1.0113203
Sum389218
Variance109.99408
MonotonicityNot monotonic
2025-12-15T06:25:15.020274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37478
 
4.8%
38477
 
4.8%
35474
 
4.7%
36456
 
4.6%
34447
 
4.5%
33442
 
4.4%
40432
 
4.3%
39423
 
4.2%
32418
 
4.2%
31404
 
4.0%
Other values (60)5549
55.5%
ValueCountFrequency (%)
1822
 
0.2%
1927
 
0.3%
2040
 
0.4%
2153
 
0.5%
2284
0.8%
2399
1.0%
24132
1.3%
25154
1.5%
26200
2.0%
27209
2.1%
ValueCountFrequency (%)
922
 
< 0.1%
881
 
< 0.1%
851
 
< 0.1%
842
 
< 0.1%
831
 
< 0.1%
821
 
< 0.1%
814
< 0.1%
803
< 0.1%
794
< 0.1%
785
0.1%

Tenure
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0128
Minimum0
Maximum10
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:25:15.134154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8921744
Coefficient of variation (CV)0.57695786
Kurtosis-1.1652252
Mean5.0128
Median Absolute Deviation (MAD)2
Skewness0.010991458
Sum50128
Variance8.3646726
MonotonicityNot monotonic
2025-12-15T06:25:15.228520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
21048
10.5%
11035
10.3%
71028
10.3%
81025
10.2%
51012
10.1%
31009
10.1%
4989
9.9%
9984
9.8%
6967
9.7%
10490
4.9%
ValueCountFrequency (%)
0413
 
4.1%
11035
10.3%
21048
10.5%
31009
10.1%
4989
9.9%
51012
10.1%
6967
9.7%
71028
10.3%
81025
10.2%
9984
9.8%
ValueCountFrequency (%)
10490
4.9%
9984
9.8%
81025
10.2%
71028
10.3%
6967
9.7%
51012
10.1%
4989
9.9%
31009
10.1%
21048
10.5%
11035
10.3%

Balance
Real number (ℝ)

High correlation  Zeros 

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76485.889
Minimum0
Maximum250898.09
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:25:15.358751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median97198.54
Q3127644.24
95-th percentile162711.67
Maximum250898.09
Range250898.09
Interquartile range (IQR)127644.24

Descriptive statistics

Standard deviation62397.405
Coefficient of variation (CV)0.81580283
Kurtosis-1.4894118
Mean76485.889
Median Absolute Deviation (MAD)46766.79
Skewness-0.14110871
Sum7.6485889 × 108
Variance3.8934362 × 109
MonotonicityNot monotonic
2025-12-15T06:25:15.522167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03617
36.2%
130170.822
 
< 0.1%
105473.742
 
< 0.1%
159397.751
 
< 0.1%
144238.71
 
< 0.1%
112262.841
 
< 0.1%
109106.81
 
< 0.1%
142147.321
 
< 0.1%
109109.331
 
< 0.1%
146587.31
 
< 0.1%
Other values (6372)6372
63.7%
ValueCountFrequency (%)
03617
36.2%
3768.691
 
< 0.1%
12459.191
 
< 0.1%
14262.81
 
< 0.1%
16893.591
 
< 0.1%
23503.311
 
< 0.1%
24043.451
 
< 0.1%
27288.431
 
< 0.1%
27517.151
 
< 0.1%
27755.971
 
< 0.1%
ValueCountFrequency (%)
250898.091
< 0.1%
238387.561
< 0.1%
222267.631
< 0.1%
221532.81
< 0.1%
216109.881
< 0.1%
214346.961
< 0.1%
213146.21
< 0.1%
212778.21
< 0.1%
212696.321
< 0.1%
212692.971
< 0.1%

NumOfProducts
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5084 
2
4590 
3
 
266
4
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Length

2025-12-15T06:25:15.642991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:25:15.726471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring characters

ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15084
50.8%
24590
45.9%
3266
 
2.7%
460
 
0.6%

HasCrCard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
7055 
0
2945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Length

2025-12-15T06:25:15.829201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:25:15.909557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring characters

ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17055
70.5%
02945
29.4%

IsActiveMember
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5151 
0
4849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Length

2025-12-15T06:25:15.998730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:25:16.079113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring characters

ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15151
51.5%
04849
48.5%

EstimatedSalary
Real number (ℝ)

High correlation 

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100090.24
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:25:16.179084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9851.8185
Q151002.11
median100193.91
Q3149388.25
95-th percentile190155.38
Maximum199992.48
Range199980.9
Interquartile range (IQR)98386.137

Descriptive statistics

Standard deviation57510.493
Coefficient of variation (CV)0.57458642
Kurtosis-1.1815184
Mean100090.24
Median Absolute Deviation (MAD)49198.15
Skewness0.0020853577
Sum1.0009024 × 109
Variance3.3074568 × 109
MonotonicityNot monotonic
2025-12-15T06:25:16.325036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.922
 
< 0.1%
121505.611
 
< 0.1%
89874.821
 
< 0.1%
72500.681
 
< 0.1%
182692.81
 
< 0.1%
4993.941
 
< 0.1%
124964.821
 
< 0.1%
161971.421
 
< 0.1%
3729.891
 
< 0.1%
55313.441
 
< 0.1%
Other values (9989)9989
99.9%
ValueCountFrequency (%)
11.581
< 0.1%
90.071
< 0.1%
91.751
< 0.1%
96.271
< 0.1%
106.671
< 0.1%
123.071
< 0.1%
142.811
< 0.1%
143.341
< 0.1%
178.191
< 0.1%
216.271
< 0.1%
ValueCountFrequency (%)
199992.481
< 0.1%
199970.741
< 0.1%
199953.331
< 0.1%
199929.171
< 0.1%
199909.321
< 0.1%
199862.751
< 0.1%
199857.471
< 0.1%
199841.321
< 0.1%
199808.11
< 0.1%
199805.631
< 0.1%

Exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
7963 
1
2037 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Length

2025-12-15T06:25:16.466882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:25:16.555831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Most occurring characters

ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07963
79.6%
12037
 
20.4%

BalanceSalaryRatio
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct6384
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8787029
Minimum0
Maximum10614.655
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:25:16.655950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.74700231
Q31.514022
95-th percentile7.2495003
Maximum10614.655
Range10614.655
Interquartile range (IQR)1.514022

Descriptive statistics

Standard deviation108.33726
Coefficient of variation (CV)27.931311
Kurtosis9208.1391
Mean3.8787029
Median Absolute Deviation (MAD)0.74700231
Skewness94.264022
Sum38787.029
Variance11736.962
MonotonicityNot monotonic
2025-12-15T06:25:16.794544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03617
36.2%
0.86528293621
 
< 0.1%
1.8359612631
 
< 0.1%
2.7806857541
 
< 0.1%
0.78220654951
 
< 0.1%
8.7205343741
 
< 0.1%
0.89939652691
 
< 0.1%
3.5997562811
 
< 0.1%
21.84834621
 
< 0.1%
1.631016341
 
< 0.1%
Other values (6374)6374
63.7%
ValueCountFrequency (%)
03617
36.2%
0.021284188811
 
< 0.1%
0.079465535931
 
< 0.1%
0.13836707811
 
< 0.1%
0.14161411631
 
< 0.1%
0.18099648441
 
< 0.1%
0.18751424981
 
< 0.1%
0.19258177791
 
< 0.1%
0.20099263741
 
< 0.1%
0.20537874131
 
< 0.1%
ValueCountFrequency (%)
10614.655441
< 0.1%
1326.1027791
< 0.1%
856.06410981
< 0.1%
611.2689411
< 0.1%
437.98084161
< 0.1%
353.22962481
< 0.1%
349.52198731
< 0.1%
345.34233271
< 0.1%
321.35876871
< 0.1%
291.14223131
< 0.1%

HighBalanceFlag
Categorical

High correlation  Uniform 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
5000 
1
5000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
05000
50.0%
15000
50.0%

Length

2025-12-15T06:25:16.920595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:25:16.993915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
05000
50.0%
15000
50.0%

Most occurring characters

ValueCountFrequency (%)
05000
50.0%
15000
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05000
50.0%
15000
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05000
50.0%
15000
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05000
50.0%
15000
50.0%

MultipleProducts
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
5084 
1
4916 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
05084
50.8%
14916
49.2%

Length

2025-12-15T06:25:17.082121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:25:17.154900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
05084
50.8%
14916
49.2%

Most occurring characters

ValueCountFrequency (%)
05084
50.8%
14916
49.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05084
50.8%
14916
49.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05084
50.8%
14916
49.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05084
50.8%
14916
49.2%

Age_Balance_interaction
Real number (ℝ)

High correlation 

Distinct6450
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2995530.9
Minimum18
Maximum13797027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-15T06:25:17.250661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile29
Q141
median3360241.8
Q34911073.1
95-th percentile7309047.8
Maximum13797027
Range13797009
Interquartile range (IQR)4911032.1

Descriptive statistics

Standard deviation2646816.1
Coefficient of variation (CV)0.88358832
Kurtosis-0.68592952
Mean2995530.9
Median Absolute Deviation (MAD)2508171
Skewness0.33313053
Sum2.9955309 × 1010
Variance7.0056354 × 1012
MonotonicityNot monotonic
2025-12-15T06:25:17.395260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34178
 
1.8%
36176
 
1.8%
38175
 
1.8%
35172
 
1.7%
37168
 
1.7%
39163
 
1.6%
33162
 
1.6%
32156
 
1.6%
40153
 
1.5%
29147
 
1.5%
Other values (6440)8350
83.5%
ValueCountFrequency (%)
188
 
0.1%
1911
 
0.1%
2017
 
0.2%
2120
 
0.2%
2236
0.4%
2335
0.4%
2452
0.5%
2556
0.6%
2666
0.7%
2786
0.9%
ValueCountFrequency (%)
13797026.941
< 0.1%
13588147.921
< 0.1%
13002683.691
< 0.1%
12903974.971
< 0.1%
12678472.621
< 0.1%
12534431.041
< 0.1%
12481824.321
< 0.1%
12427717.11
< 0.1%
12234531.681
< 0.1%
12087966.551
< 0.1%

LowSalary_HighBalance
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
7516 
1
2484 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
07516
75.2%
12484
 
24.8%

Length

2025-12-15T06:25:17.522614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:25:17.617035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
07516
75.2%
12484
 
24.8%

Most occurring characters

ValueCountFrequency (%)
07516
75.2%
12484
 
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07516
75.2%
12484
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07516
75.2%
12484
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07516
75.2%
12484
 
24.8%

Geography_LE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
5014 
1
2509 
2
2477 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
05014
50.1%
12509
25.1%
22477
24.8%

Length

2025-12-15T06:25:17.708357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:25:17.792115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
05014
50.1%
12509
25.1%
22477
24.8%

Most occurring characters

ValueCountFrequency (%)
05014
50.1%
12509
25.1%
22477
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05014
50.1%
12509
25.1%
22477
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05014
50.1%
12509
25.1%
22477
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05014
50.1%
12509
25.1%
22477
24.8%

Gender_LE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5457 
0
4543 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
15457
54.6%
04543
45.4%

Length

2025-12-15T06:25:17.901482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-15T06:25:17.979297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15457
54.6%
04543
45.4%

Most occurring characters

ValueCountFrequency (%)
15457
54.6%
04543
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15457
54.6%
04543
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15457
54.6%
04543
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15457
54.6%
04543
45.4%

AgeGroup_25-34
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
6458 
True
3542 
ValueCountFrequency (%)
False6458
64.6%
True3542
35.4%
2025-12-15T06:25:18.031672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

AgeGroup_35-49
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
5414 
True
4586 
ValueCountFrequency (%)
False5414
54.1%
True4586
45.9%
2025-12-15T06:25:18.081840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

AgeGroup_50-64
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
9003 
True
997 
ValueCountFrequency (%)
False9003
90.0%
True997
 
10.0%
2025-12-15T06:25:18.131648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

AgeGroup_65+
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
9736 
True
 
264
ValueCountFrequency (%)
False9736
97.4%
True264
 
2.6%
2025-12-15T06:25:18.179393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

TenureBucket_2-3
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7943 
True
2057 
ValueCountFrequency (%)
False7943
79.4%
True2057
 
20.6%
2025-12-15T06:25:18.226537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

TenureBucket_4-6
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7032 
True
2968 
ValueCountFrequency (%)
False7032
70.3%
True2968
29.7%
2025-12-15T06:25:18.278315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

TenureBucket_7-10
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
6473 
True
3527 
ValueCountFrequency (%)
False6473
64.7%
True3527
35.3%
2025-12-15T06:25:18.329983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CreditScoreBucket_Med
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7499 
True
2501 
ValueCountFrequency (%)
False7499
75.0%
True2501
 
25.0%
2025-12-15T06:25:18.380534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CreditScoreBucket_High
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7498 
True
2502 
ValueCountFrequency (%)
False7498
75.0%
True2502
 
25.0%
2025-12-15T06:25:18.430400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CreditScoreBucket_VeryHigh
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7537 
True
2463 
ValueCountFrequency (%)
False7537
75.4%
True2463
 
24.6%
2025-12-15T06:25:18.479159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-12-15T06:25:13.059024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:03.156503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:04.496045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:05.885490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:07.995246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:10.599538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:12.163007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:13.173397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:03.327181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:04.679712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:06.075544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:08.348701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:10.805387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:12.375669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:13.300054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:03.508980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:04.914402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:06.251626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:08.907955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:11.032688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:12.489518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:13.442555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:03.723450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:05.088826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:06.415282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:09.335381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:11.288366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:12.594876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:13.562547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:03.906708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:05.274400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:06.583019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:09.593578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:11.504557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:12.701970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:13.697383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:04.088931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:05.480920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:07.102714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:09.857770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:11.752664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:12.825387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:13.820349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:04.290050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:05.667953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:07.617465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:10.151079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:11.947164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-15T06:25:12.934618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-15T06:25:18.584963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAgeGroup_25-34AgeGroup_35-49AgeGroup_50-64AgeGroup_65+Age_Balance_interactionBalanceBalanceSalaryRatioCreditScoreCreditScoreBucket_HighCreditScoreBucket_MedCreditScoreBucket_VeryHighEstimatedSalaryExitedGender_LEGeography_LEHasCrCardHighBalanceFlagIsActiveMemberLowSalary_HighBalanceMultipleProductsNumOfProductsTenureTenureBucket_2-3TenureBucket_4-6TenureBucket_7-10
Age1.0000.7920.7520.8300.8940.4440.0330.036-0.0080.0000.0000.000-0.0020.3750.0260.0500.0130.0390.1440.0140.0860.087-0.0100.0000.0000.000
AgeGroup_25-340.7921.0000.6810.2460.1210.3350.0110.0000.0190.0000.0070.0000.0280.2180.0180.0460.0170.0200.0000.0100.0420.0870.0180.0000.0000.000
AgeGroup_35-490.7520.6811.0000.3060.1510.2880.0000.0020.0000.0000.0000.0000.0280.0950.0000.0280.0010.0000.0680.0000.0000.0340.0260.0000.0000.007
AgeGroup_50-640.8300.2460.3061.0000.0530.3760.0390.0270.0330.0040.0000.0000.0040.2690.0180.0290.0060.0290.0590.0260.0700.1120.0190.0000.0200.000
AgeGroup_65+0.8940.1210.1510.0531.0000.3700.0000.0000.0100.0000.0000.0150.0000.0260.0000.0000.0000.0100.1190.0000.0000.0000.0130.0000.0050.000
Age_Balance_interaction0.4440.3350.2880.3760.3701.0000.8910.7770.0030.0370.0090.0200.0080.2400.0250.3090.0000.8400.0620.4890.3860.231-0.0120.0100.0000.011
Balance0.0330.0110.0000.0390.0000.8911.0000.8220.0060.0000.0000.0000.0120.1410.0000.3150.0390.9630.0140.5500.3880.230-0.0100.0360.0300.024
BalanceSalaryRatio0.0360.0000.0020.0270.0000.7770.8221.0000.0070.0120.0120.000-0.3690.0240.0020.0100.0090.0000.0010.0200.0000.000-0.0180.0000.0090.006
CreditScore-0.0080.0190.0000.0330.0100.0030.0060.0071.0000.8670.8780.8940.0010.0860.0000.0180.0000.0160.0250.0000.0080.0170.0010.0000.0000.000
CreditScoreBucket_High0.0000.0000.0000.0040.0000.0370.0000.0120.8671.0000.3330.3300.0000.0290.0000.0220.0000.0000.0090.0000.0000.0000.0000.0000.0000.000
CreditScoreBucket_Med0.0000.0070.0000.0000.0000.0090.0000.0120.8780.3331.0000.3300.0000.0000.0000.0240.0180.0000.0000.0000.0120.0160.0000.0000.0000.011
CreditScoreBucket_VeryHigh0.0000.0000.0000.0000.0150.0200.0000.0000.8940.3300.3301.0000.0170.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
EstimatedSalary-0.0020.0280.0280.0040.0000.0080.012-0.3690.0010.0000.0000.0171.0000.0000.0210.0170.0000.0000.0250.5730.0000.0190.0080.0090.0020.000
Exited0.3750.2180.0950.2690.0260.2400.1410.0240.0860.0290.0000.0000.0001.0000.1060.1730.0000.1140.1560.0630.1850.3870.0220.0000.0000.012
Gender_LE0.0260.0180.0000.0180.0000.0250.0000.0020.0000.0000.0000.0000.0210.1061.0000.0220.0000.0080.0200.0170.0000.0420.0250.0000.0000.012
Geography_LE0.0500.0460.0280.0290.0000.3090.3150.0100.0180.0220.0240.0090.0170.1730.0221.0000.0050.3630.0180.2080.0320.0470.0280.0000.0000.000
HasCrCard0.0130.0170.0010.0060.0000.0000.0390.0090.0000.0000.0180.0000.0000.0000.0000.0051.0000.0000.0060.0000.0000.0000.0260.0000.0000.007
HighBalanceFlag0.0390.0200.0000.0290.0100.8400.9630.0000.0160.0000.0000.0000.0000.1140.0080.3630.0001.0000.0070.5750.3020.3100.0000.0000.0210.000
IsActiveMember0.1440.0000.0680.0590.1190.0620.0140.0010.0250.0090.0000.0000.0250.1560.0200.0180.0060.0071.0000.0000.0200.0380.0210.0060.0020.015
LowSalary_HighBalance0.0140.0100.0000.0260.0000.4890.5500.0200.0000.0000.0000.0000.5730.0630.0170.2080.0000.5750.0001.0000.1840.1870.0000.0220.0000.010
MultipleProducts0.0860.0420.0000.0700.0000.3860.3880.0000.0080.0000.0120.0000.0000.1850.0000.0320.0000.3020.0200.1841.0001.0000.0450.0190.0000.000
NumOfProducts0.0870.0870.0340.1120.0000.2310.2300.0000.0170.0000.0160.0000.0190.3870.0420.0470.0000.3100.0380.1871.0001.0000.0350.0260.0000.000
Tenure-0.0100.0180.0260.0190.013-0.012-0.010-0.0180.0010.0000.0000.0000.0080.0220.0250.0280.0260.0000.0210.0000.0450.0351.0001.0001.0001.000
TenureBucket_2-30.0000.0000.0000.0000.0000.0100.0360.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0060.0220.0190.0261.0001.0000.3300.375
TenureBucket_4-60.0000.0000.0000.0200.0050.0000.0300.0090.0000.0000.0000.0000.0020.0000.0000.0000.0000.0210.0020.0000.0000.0001.0000.3301.0000.479
TenureBucket_7-100.0000.0000.0070.0000.0000.0110.0240.0060.0000.0000.0110.0000.0000.0120.0120.0000.0070.0000.0150.0100.0000.0001.0000.3750.4791.000

Missing values

2025-12-15T06:25:14.053758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-15T06:25:14.333512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CreditScoreAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedBalanceSalaryRatioHighBalanceFlagMultipleProductsAge_Balance_interactionLowSalary_HighBalanceGeography_LEGender_LEAgeGroup_25-34AgeGroup_35-49AgeGroup_50-64AgeGroup_65+TenureBucket_2-3TenureBucket_4-6TenureBucket_7-10CreditScoreBucket_MedCreditScoreBucket_HighCreditScoreBucket_VeryHigh
06194220.00111101348.8810.0000000042.00000FalseTrueFalseFalseTrueFalseFalseTrueFalseFalse
160841183807.86101112542.5800.744677003436163.26020FalseTrueFalseFalseFalseFalseFalseTrueFalseFalse
2502428159660.80310113931.5711.401375116705795.60000FalseTrueFalseFalseFalseFalseTrueFalseFalseFalse
36993910.0020093826.6300.0000000139.00000FalseTrueFalseFalseFalseFalseFalseFalseTrueFalse
4850432125510.8211179084.1001.587055105397008.26120FalseTrueFalseFalseTrueFalseFalseFalseFalseTrue
5645448113755.78210149756.7110.759604115005298.32021FalseTrueFalseFalseFalseFalseTrueTrueFalseFalse
68225070.0021110062.8000.0000000150.00001FalseTrueFalseFalseFalseFalseTrueFalseFalseTrue
7376294115046.74410119346.8810.963969113336384.46010TrueFalseFalseFalseFalseTrueFalseFalseFalseFalse
8501444142051.0720174940.5001.895518116250291.08101FalseTrueFalseFalseFalseTrueFalseFalseFalseFalse
9684272134603.8811171725.7301.876647103634331.76101TrueFalseFalseFalseTrueFalseFalseFalseTrueFalse
CreditScoreAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedBalanceSalaryRatioHighBalanceFlagMultipleProductsAge_Balance_interactionLowSalary_HighBalanceGeography_LEGender_LEAgeGroup_25-34AgeGroup_35-49AgeGroup_50-64AgeGroup_65+TenureBucket_2-3TenureBucket_4-6TenureBucket_7-10CreditScoreBucket_MedCreditScoreBucket_HighCreditScoreBucket_VeryHigh
999071433335016.6011053667.0800.652478001155580.80011TrueFalseFalseFalseTrueFalseFalseFalseTrueFalse
999159753488381.2111069384.7111.273785004684257.13000FalseFalseTrueFalseFalseTrueFalseTrueFalseFalse
99927263620.00110195192.4000.0000000036.00021FalseTrueFalseFalseTrueFalseFalseFalseFalseTrue
9993644287155060.4111029179.5205.314015104341719.48101TrueFalseFalseFalseFalseFalseTrueTrueFalseFalse
99948002920.00200167773.5500.0000000129.00000TrueFalseFalseFalseTrueFalseFalseFalseFalseTrue
99957713950.0021096270.6400.0000000139.00001FalseTrueFalseFalseFalseTrueFalseFalseFalseTrue
9996516351057369.61111101699.7700.564108002007971.35001TrueFalseFalseFalseFalseFalseTrueFalseFalseFalse
99977093670.0010142085.5810.0000000036.00000FalseTrueFalseFalseFalseFalseTrueFalseTrueFalse
999877242375075.3121092888.5210.808230013153205.02011FalseTrueFalseFalseTrueFalseFalseFalseFalseTrue
9999792284130142.7911038190.7803.407702103644026.12100TrueFalseFalseFalseFalseTrueFalseFalseFalseTrue